Search skip region setting function generation method, search skip region setting method, and object search method

ABSTRACT

According to one embodiment, a search skip region setting function generation method includes associating, detecting, and generating. The associating associates a template used to search a model image for an object with a designated search point on the model image, and detects a designated search point similarity between the designated search point and the template. When the designated search point similarity exceeds an object detection determination threshold, the detecting detects surrounding search point similarities between a plurality of surrounding search points around the designated search point on the model image and the template. The generating generates a function required to set a search skip region of the object based on relative positions between the object and the template, which are estimated based on a distribution of the surrounding search point similarities.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is based upon and claims the benefit of priority fromJapanese Patent Application No. 2010-248557, filed Nov. 5, 2010; theentire contents of which are incorporated herein by reference.

FIELD

Embodiments described herein relate generally to a search skip regionsetting function generation method, a search skip region setting method,and an object search method.

BACKGROUND

In recent years, various techniques for detecting an object such as ahuman face from an image have been proposed. For example, the followingobject detection technique has been proposed. A template created from areference image is overlaid on an object search target image, and isscanned. At respective overlaying positions, distances (or similarvalues or similarities) between the object search target image andtemplate (or between feature amounts of the object search target imageand those of the template) are computed. Subsequently, an overlayingposition where a minimum distance is obtained (or an overlaying positionwhere a maximum similarity between the object search target image andtemplate is obtained) is output as an object detection position.

In order to accurately search for an object, for example, a template isshifted pixel by pixel with respect to an object search target image,and a similarity between the object search target image and template iscomputed in correspondence with each shift position.

However, with the above object search method, the computation load foran object search is heavy, resulting in high computing cost. That is, anobject search load is heavy. Hence, an efficiency enhancement techniqueof an object search is demanded.

BRIEF DESCRIPTION OF THE DRAWINGS

A general architecture that implements the various features of theembodiments will now be described with reference to the drawings. Thedrawings and the associated descriptions are provided to illustrate theembodiments and not to limit the scope of the invention.

FIG. 1 is a flowchart showing an example of creation processing of asearch skip region setting function according to an embodiment;

FIG. 2 is a flowchart showing an example of measurement processing of asimilarity frequency near a detection position in FIG. 1;

FIG. 3 is a flowchart showing an example of creation processing of asimilarity distribution in FIG. 1;

FIG. 4 is a flowchart showing an example of creation processing of asearch skip region setting function in FIG. 1;

FIG. 5 is a block diagram showing an example of an object search systemaccording to the embodiment;

FIG. 6 is a view showing an example of the concept of an object searchaccording to the embodiment;

FIG. 7 is a view showing an example of the concept of object (face)detection according to the embodiment;

FIG. 8 is a graph showing an example of the concept of a similaritydistribution (density) p(s|x, y) at relative coordinates (x, y)according to the embodiment;

FIG. 9 is a graph showing an example of the concept of a similaritydistribution (density) p(s|x, y) at positions separated away from anobject detection position according to the embodiment;

FIG. 10 is a graph showing an example of the concept of a similaritydistribution (density) p(s|x, y) at positions close to the objectdetection position according to the embodiment;

FIG. 11 is a graph showing an example of the concept of a search skipregion setting function sP(x, y) according to the embodiment;

FIG. 12 is a graph showing an example of the concept of a search skipregion setting function sP(x, y) according to the embodiment;

FIG. 13 is a graph showing an example of the relationship between thesearch skip region setting function sP(x, y) and search skip regionsaccording to the embodiment;

FIG. 14 is a graph showing an example of the relationship between searchpoints and search skip regions according to the embodiment;

FIG. 15 is a graph showing an example of a setting order of search skipregions on an object search target image according to the embodiment;

FIG. 16 is a graph showing an example of a setting order of search skipregions on an object search target image according to the embodiment;and

FIG. 17 is a block diagram showing an example of a digital televisionbroadcast receiver to which an object search skip region setting methodand object search skip region setting apparatus according to theembodiment are applied.

DETAILED DESCRIPTION

Various embodiments will be described hereinafter with reference to theaccompanying drawings.

In general, according to one embodiment, a search skip region settingfunction generation method includes associating, detecting, andgenerating. The associating associates a template used to search a modelimage for an object with a designated search point on the model image,and detects a designated search point similarity between the designatedsearch point and the template. When the designated search pointsimilarity exceeds an object detection determination threshold, thedetecting detects surrounding search point similarities between aplurality of surrounding search points around the designated searchpoint on the model image and the template. The generating generates afunction required to set a search skip region of the object based onrelative positions between the object and the template, which areestimated based on a distribution of the surrounding search pointsimilarities.

An object search skip region setting method of this embodiment detects,for example, a similarity distribution near an object using a learningimage (model image) in advance before an object search, creates a searchskip region setting function based on the similarity distribution, andsets a search skip region based on the search skip region settingfunction. An object search method of this embodiment sets a search skipregion on an object search target image based on the search skip regionsetting function, and searches the object search target image for anobject using a template by excluding the search skip region.

For example, an object search system 1 shown in FIG. 5 creates a searchskip region setting function, sets a search skip region on an objectsearch target image based on the search skip region setting function,and searches the object search target image for an object using atemplate by excluding the search skip region.

All of the creation processing of a search skip region setting function,the setting processing of a search skip region, and the object searchneed not be implemented by a single device. For example, a computercreates a search skip region setting function. An image processingdevice such as a digital TV mounts an object search function based onthe search skip region setting function, and searches for an object bythe object search function. That is, the image processing device sets asearch skip region on an object search target image (for example, aprogram image) based on the search skip region setting function createdby, for example, the computer, and searches the object search targetimage for an object using a template by excluding the search skipregion.

An example of the creation processing of a search skip region settingfunction, the setting processing of a search skip region, and the objectsearch by the object search system 1 shown in FIG. 5 will be describedbelow. As shown in FIG. 5, for example, the object search system 1includes an image input module 2, a similarity distribution creationmodule 3, a search skip region setting module 4, a search module 5, anda storage unit 6.

FIG. 1 is a flowchart showing an example of creation processing of asearch skip region setting function.

Initially, an example of measurement processing of a similarityfrequency near a detection position (BLOCK 100) in FIG. 1 will bedescribed with reference to the flowchart shown in FIG. 2.

For example, the image input module 2 of the object search system 1inputs a first learning image of, for example, a plurality of learningimages (model images) (BLOCK 101). The similarity distribution creationmodule 3 initializes a detection count n and frequency f(s|x, y) (BLOCK102), sets a search point (x, y) (BLOCK 103), and computes a similaritys at the search point (BLOCK 104).

That is, the similarity distribution creation module 3 scans a template22 used to search a first learning image 21 (see FIGS. 6 and 7) for anobject with respect to the first learning image 21. For example, thesimilarity distribution creation module 3 associates the template 22(see FIGS. 6 and 7) used to search the first learning image 21 (seeFIGS. 6 and 7) for an object with a first designated search point on thefirst learning image 21 to have a first positional relationship, anddetects a first designated search point similarity (similarity s)between the first designated search point on the first learning imageand the template.

If the first designated search point similarity (similarity s) does notexceed a detection threshold θ (object detection determination thresholdθ) (NO in BLOCK 105), the similarity distribution creation module 3changes the first designated search point (BLOCK 103). Then, thesimilarity distribution creation module 3 associates the template usedto search the first learning image for an object with a seconddesignated search point on the first learning image to have a secondpositional relationship, and detects a second designated search pointsimilarity (similarity s) between the second designated search point onthe first learning image and the template.

If the second designated search point similarity (similarity s) does notexceed the detection threshold θ (object detection determinationthreshold θ), the similarity distribution creation module 3 furtherchanges the second designated search point and continues similaritydetection. If the second designated search point similarity (similaritys) exceeds the detection threshold θ (object detection determinationthreshold θ), the similarity distribution creation module 3 determinesthat an object is detected in the second positional relationship.

As described above, if the similarity s exceeds the detection thresholdθ (object detection determination threshold θ) (YES in BLOCK 105), thesimilarity distribution creation module 3 determines that an object isdetected, and computes similarities near the detection position. Thesimilarity distribution creation module 3 executes processing for addinga frequency f( ) associated with relative coordinates (xR, yR) from theobject detection position and the similarity s for all relativecoordinates and all search points (BLOCK 106 to BLOCK 110).

For example, when the first designated search point similarity(similarity s) exceeds the detection threshold θ (object detectiondetermination threshold θ) in the first positional relationship, thesimilarity distribution creation module 3 detects surrounding searchpoint similarities between a plurality of first surrounding searchpoints around the first designated search point on the first learningimage in the first positional relationship, and the template.

Upon completion of measurement of a similarity frequency for the firstlearning image with the above processing, the image input module 2inputs a second learning image of the plurality of learning images, andthe similarity distribution creation module 3 measures theaforementioned similarity frequency for the second learning image (BLOCK101 to BLOCK 111). Subsequently, the similarity distribution creationmodule 3 similarly measures the aforementioned similarity frequenciesfor all learning images (BLOCK 101 to BLOCK 111).

Also, a plurality of types of templates may be prepared, and thesimilarity distribution creation module 3 may measure similarityfrequencies using the respective templates. The plurality of types oftemplates are those used to detect, for example, different objects, andobjects having different sizes.

The template will be supplemented. A template used to detect a face(object) means dictionary patterns of a subspace method, whichrespectively correspond to a face, eyes, and nose, as described in, forexample, Jpn. Pat. Appln. KOKAI Publication No. 2003-346158.Alternatively, a template used to detect a face (object) is a modelwhich expresses static features of each individual face of registeredface images using sets of allocations of preset feature points and imagefeature amounts near these feature points, as described in, for example,Jpn. Pat. Appln. KOKAI Publication No. 2007-249394. Alternatively, atemplate used to detect a face (object) is a set of model data Hincluding pieces of information (for example, Gabor waveletcoefficients) CA(x-) of images near feature points plotted on aregistered face image, pieces of feature point allocation informationx-, and person IDn, as described in, for example, Jpn. Pat. Appln. KOKAIPublication No. 2005-208850. Note that the template may be theaforementioned dictionary patterns or model data, or may be an image(template image).

An example of creation processing of a similarity distribution (BLOCK200) in FIG. 1 will be described below with reference to the flowchartshown in FIG. 3.

The similarity distribution creation module 3 executes processing forcomputing P( ) by dividing the frequency f( ) computed in BLOCK 100 bythe detection count n for all relative coordinates (BLOCK 201 to BLOCK205). FIGS. 8, 9, and 10 are graphs showing examples of the concept ofP(s|xR, yR). When positions are closer to an object position, a regionhaving greater similarities s has a higher frequency, as shown in FIG.10. When positions are farther away from an object position, a regionhaving smaller similarities s has a higher frequency, as shown in FIG.9.

An example of creation processing of a search skip region settingfunction in FIG. 1 (BLOCK 300) will be described below with reference tothe flowchart shown in FIG. 4.

The similarity distribution creation module 3 executes processing forcumulatively adding s from s=0 to s=P(s|x, y), and setting s immediatelybefore a detection error ratio P (allowable detection error ratio P) isexceeded to be SP(x, y) for all relative coordinates (x, y) (BLOCK 301to BLOCK 306), and the search skip region setting module 4 generates asearch skip region setting function SP(x, y).

That is, the search skip region setting module 4 estimates distancesbetween an object and a template based on the aforementioned surroundingsearch point similarity distribution, and generates a search skip regionsetting function used to set a search skip region based on the estimateddistances. In this case, the distance means a relative position, and acriterion of the distance changes depending on directions. Morespecifically, the search skip region setting module 4 estimates objectdetection error ratios at the first surrounding search points based onthe estimated distances (estimated relative positions), and generatesthe search skip region setting function based on the estimated detectionerror ratios. More specifically, the search skip region setting module 4generates a search skip region setting function which compares theallowable detection error ratio that allows object detection errors andthe estimated detection error ratios, and sets a region where theestimated detection error ratios are less than the allowable detectionerror ratio as a search skip region.

FIG. 11 is a graph showing an example of the concept of SP(x, y). When adetection error ratio P (allowable detection error ratio P) is small, acurve approaches the center, thus narrowing down a search skip region.Conversely, when the detection error ratio P (allowable detection errorratio P) is large, the curve is separated away from the center, thusbroadening a search skip region. For example, when a sufficiently lowpossibility of object detection errors is to be set, the allowabledetection error ratio P is set to be a vary small value (for example,0%<P<5%), and a search skip region is narrowed down, thus setting a lowpossibility of object detection errors. When importance is attached to areduction of an object search load rather than a lower possibility ofobject detection errors, the allowable detection error ratio P is set tobe a relatively low value (for example, 5%≦P<10%), and a search skipregion is broadened, thus reducing the object search load whilepreventing object detection errors.

FIG. 12 is a graph showing an example of the concept used to explain thesearch skip region setting function SP(x, y). FIG. 12 shows an exampleof lower limits of similarities at relative coordinates from an objectdetection position (0, 0) (to have the detection error ratio P). FIG. 12shows a hill-like shape having the center as a top peak. When an objectis detected at the object detection position (0, 0), a similarity at (x,y) is greater than or equal to SP(x, y) at a probability of 1−P.

FIG. 13 shows an example of a graph obtained by inverting FIG. 12 inassociation with x and y. When a similarity s is obtained at a certainposition, letting (xR, yR) be relative coordinates from that position,an object detection probability in a region of SP(−xR, −yR)>s is lessthan or equal to the detection error ratio P (allowable detection errorratio P). Therefore, by setting the detection error ratio P (allowabledetection error ratio P) to be a sufficiently small value, a search inthis region is skipped to reduce a search computation volume.

The aforementioned mathematical definitions will be summarized below.S: Similarity  (1)P(s|x,y): Similarity distribution at relative coordinates (x,y)  (2)p(s|x,y): Similarity distribution (density) at relative coordinates(x,y)  (3)P: Detection error ratio  (4)s _(P)(x,y):Similarity threshold according to Detection error ratioP  (5)s≦s _(P)(x,y): Search skip region  (6)∫₀ ^(s) p(s|x,y)ds=1  (7)

$\begin{matrix}{{\sum\limits_{s = 0}^{s_{\max}}{P\left( {{s❘x},y} \right)}} = 1} & (8) \\{{\sum\limits_{s = 0}^{s_{P}{({x,y})}}{P\left( {{s❘x},y} \right)}} = {P < 1}} & (9)\end{matrix}$

FIG. 14 is a graph showing an example of the concept of a search. A “+”mark in FIG. 14 indicates a search point. The search skip region settingmodule 4 sets search skip regions (hatched regions in FIG. 14) near thesearch points after search similarity computations.

For example, the image input module 2 inputs an object search targetimage (for example, a program image). The similarity distributioncreation module 3 associates the object search target image with atemplate to have a predetermined positional relationship (to associate afirst designated search point of the object search target image with thetemplate), and detects a designated search point similarity between thefirst designated search point on the object search target image and thetemplate. Furthermore, the similarity distribution creation module 3detects surrounding search point similarities between a plurality offirst surrounding search points around the first designated search pointon the object search target image, and the template. Based on thedetected designated search point similarity and surrounding search pointsimilarities, when the similarities between the first designated searchpoint and the plurality of surrounding search points on the objectsearch target image, and the template are lower than a threshold θ(object non-detection determination threshold θ), the similaritydistribution creation module 3 determines that no object exists near (inthe predetermined region of) the first designated search point on theobject search target image at a high possibility, and the search skipregion setting module 4 sets a search skip region based on the searchskip region setting function with reference to the first designatedsearch point.

Since a search skip region corresponding to one search point depends onsimilarities, allocations of search points have to be dynamically set.For example, as shown in FIG. 15, when similarities between a firstdesignated search point A10 and a plurality of surrounding search pointson an object search target image, and a template do not exceed thethreshold θ (object non-detection determination threshold θ), the searchskip region setting module 4 sets a first search skip region A11 basedon the search skip region setting function with reference to the firstdesignated search point A10. Next, when similarities between a seconddesignated search point A20 (which falls outside the first search skipregion A11 and is adjacent to the first search skip region A11) and aplurality of surrounding search points, and the template do not exceedthe threshold θ (object non-detection determination threshold θ), thesearch skip region setting module 4 sets a second search skip region A21based on the search skip region setting function with reference to thesecond designated search point A20. Next, when similarities between athird designated search point A30 (which falls outside the first searchskip region A11 and the second search skip region A21, is farthest fromthe first search skip region A11, and is adjacent to the second searchskip region A21) and a plurality of surrounding search points, and thetemplate do not exceed the threshold θ (object non-detectiondetermination threshold θ), the search skip region setting module 4 setsa third search skip region A31 based on the search skip region settingfunction with reference to the third designated search point A30. Inthis manner, the search skip region setting module 4 can set one or moresearch skip regions on the object search target image.

Alternatively, as shown in FIG. 16, when similarities between a firstdesignated search point A10 and a plurality of surrounding search pointson an object search target image, and a template do not exceed thethreshold θ (object non-detection determination threshold θ), the searchskip region setting module 4 sets a first search skip region A11 basedon the search skip region setting function with reference to the firstdesignated search point A10. Next, when similarities between a seconddesignated search point A20 (which falls outside the first search skipregion A11 and is separated from the first search skip region A11 by apredetermined distance or more) and a plurality of surrounding searchpoints, and the template do not exceed the threshold θ (objectnon-detection determination threshold θ), the search skip region settingmodule 4 sets a second search skip region A21 based on the search skipregion setting function with reference to the second designated searchpoint A20. Next, when similarities between a third designated searchpoint A30 (which falls outside the first search skip region A11 and thesecond search skip region A21, and is separated from the first searchskip region A11 and the second search skip region A21 by a predetermineddistance or more) and a plurality of surrounding search points, and thetemplate do not exceed the threshold θ (object non-detectiondetermination threshold θ), the search skip region setting module 4 setsa third search skip region A31 based on the search skip region settingfunction with reference to the third designated search point A30. Inthis manner, the search skip region setting module 4 can set one or moresearch skip regions on the object search target image as a first stage.Furthermore, when similarities between a fourth designated search pointA40 (which falls outside the first search skip region A11, the secondsearch skip region A21, and the third search skip region A31, and is,for example, farthest from these regions) and a plurality of surroundingsearch points, and the template do not exceed the threshold θ (objectnon-detection determination threshold θ), the search skip region settingmodule 4 sets a fourth search skip region A41 based on the search skipregion setting function with reference to the fourth designated searchpoint A40. In this manner, the search skip region setting module 4 canset one or more search skip regions on the object search target image asa second stage. By repeating the aforementioned processing, the searchskip region setting module 4 can efficiently set a plurality of searchskip regions on the object search target image.

As described above, the search skip region setting module 4 sets aplurality of search skip regions to cover the object search targetimage. Subsequently, the search module 5 conducts an object search foran exclusion region obtained by excluding the search skip regions froman object search target image region. That is, the search module 5overlays and scans a template on the exclusion region obtained byexcluding the search skip regions from the object search target imageregion, thus searching for an object. In this manner, an object searchcan be conducted more efficiently than a case in which an object searchis conducted by shifting a template pixel by pixel for the entire objectsearch target image region.

According to the object search skip region setting method of thisembodiment, object search processing can be speeded up while suppressingobject detection errors.

An application example of the object search skip region setting methodand object search skip region setting apparatus according to thisembodiment will be described below. FIG. 17 is a schematic block diagramshowing an example of the arrangement of a digital television broadcastreceiver to which the object search skip region setting method andobject search skip region setting apparatus according to this embodimentare applied.

The basic arrangement of a digital television broadcast receiver 100will be briefly described below. As shown in FIG. 17, the digitaltelevision broadcast receiver 100 includes an input terminal 102, atuner unit 103, external input terminals 104 to 107, a signal processingmodule 108, a controller 110, an OSD signal generation module 111, agraphic processing module 112, a video processing module 113, and anaudio processing module 114.

A terrestrial digital television broadcast signal received by aterrestrial broadcast receiving antenna 101 is supplied to the tunerunit 103 via the input terminal 102. The tuner unit 103 tunes a signalof a designated channel from the broadcast signal, and outputs thatsignal to the signal processing module 108. The signal processing module108 separates video and audio signals from the tuned signal of thedesignated channel, outputs the video signal to the graphic processingmodule 112, and outputs the audio signal to the audio processing module114.

The graphic processing module 112 superimposes an on-screen display(OSD) signal generated by the OSD signal generation module 111 on thevideo signal, as needed, and outputs that video signal. The graphicprocessing module 112 can also selectively output the video signal fromthe signal processing module 108 and the OSD signal from the OSD signalgeneration module 111.

The video signal output from the graphic processing module 112 issupplied to the video processing module 113. The video signal processedby the video processing module 113 is supplied to a video display unit141. The video display unit 141 displays an image based on the videosignal. The audio processing module 114 converts the audio signal intoan analog audio signal that can be output from a loudspeaker 142, andoutputs the converted signal to the loudspeaker 142.

The digital television broadcast receiver 100 is systematicallycontrolled by the controller 110. The controller 110 is configured by,for example a central processing unit (CPU), which controls theoperations of the respective modules upon reception of signals (variousinstructions) from, for example, a remote controller. Also, thecontroller 110 includes a read-only memory (ROM) 1101 which storescontrol programs to be executed by the CPU, a random access memory (RAM)1102 which provides work areas to the CPU, and a nonvolatile memory 1103which stores, for example, various kinds of setting and controlinformation.

For example, the aforementioned video processing module 113 includes thesimilarity distribution creation module 3, search skip region settingmodule 4, and search module 5 shown in FIG. 5. The search skip regionsetting module 4 sets search skip regions on an input image (programimage) based on the search skip region setting function. The searchmodule 5 conducts an object search for an exclusion region obtained byexcluding the search skip regions from an input image region, and candetect an object from the input image.

The significance of detection of an object from an input image will bebriefly explained below. The video processing module 113 has a functionof converting, for example, a two-dimensional (2D) image into athree-dimensional (3D) image (2D/3D conversion). For example, in the2D/3D conversion processing, a 2D image is analyzed to detect eachobject such as a person in the 2D image and to detect an anteroposteriorrelation (depth) of that object. The object is deformed as needed togenerate a 3D image. The aforementioned object detection can be appliedto such 2D/3D conversion processing.

The video processing module 113 has an image quality enhancementfunction which enhances the image quality of an input image. Forexample, the image quality enhancement processing includessuper-resolution processing. Super-resolution processing converts alow-resolution (first-resolution) image signal to a high-resolution(second-resolution) image signal by estimating new pixel values on thebasis of the first-resolution image signal in order to increase thenumber of pixels. With such super-resolution processing, for example, anobject is detected, and a super-resolution processing effect may beemphasized or de-emphasized depending on the type of object. Theaforementioned object detection can be applied to such image qualityenhancement processing.

Note that object detection is not limited to applications to theaforementioned 2D/3D conversion processing and image quality enhancementprocessing, but can be applied to various kinds of image processing thatrequire object detection. That is, the aforementioned object search skipregion setting method can be applied to the aforementioned 2D/3Dconversion processing and image quality enhancement processing, and canalso be applied to various kinds of image processing.

According to at least one embodiment described above, the search skipregion setting function generation method, search skip region settingmethod, object search method, search skip region setting functiongeneration apparatus, search skip region setting apparatus, and objectsearch apparatus, which can improve the object search efficiency, can beprovided.

The various modules of the embodiments described herein can beimplemented as software applications, hardware and/or software modules,or components on one or more computers, such as servers. While thevarious modules are illustrated separately, they may share some or allof the same underlying logic or code.

While certain embodiments have been described, these embodiments havebeen presented by way of example only, and are not intended to limit thescope of the inventions. Indeed, the novel embodiments described hereinmay be embodied in a variety of other forms; furthermore, variousomissions, substitutions and changes in the form of the embodimentsdescribed herein may be made without departing from the spirit of theinventions. The accompanying claims and their equivalents are intendedto cover such forms or modifications as would fall within the scope andspirit of the inventions.

1. A search skip region setting function generation method comprising: associating a template used to search a model image for an object with a designated search point on the model image, and detecting a designated search point similarity between the designated search point and the template; changing the designated search point when the designated search point similarity does not exceed an object detection determination threshold, and detecting surrounding search point similarities between a plurality of surrounding search points around the designated search point on the model image and the template when the designated search point similarity exceeds the object detection determination threshold; and estimating a distance between the object and the template based on a high-low frequency of the surrounding search point similarities, estimating a detection error ratio of the object at a first surrounding search point based on the estimated distance, comparing an allowable detection error ratio that allows a detection error of the object with the estimated detection error ratio, and generating a function required to set a region where the estimated detection error ratio are less than the allowable detection error ratio as the search skip region of the object.
 2. The method of claim 1, wherein the generating comprises generating the function required to set a first search skip region of a first size based on a first estimated distance, and to set a second search skip region of a second size greater than the first size based on a second estimated distance longer than the first estimated distance.
 3. The method of claim 1, wherein the generating comprises generating the function required to set a first search skip region of a first size based on first estimated detection error ratios, and to set a second search skip region of a second size greater than the first size based on second estimated detection error ratios lower than the first estimated detection error ratios.
 4. A search skip region setting method comprising: setting the search skip region based on the function generated by a search skip region setting function generation method of claim
 1. 5. An object search method comprising: searching, for the object, an exclusion region obtained by excluding the search skip region, which is set on an object search target image by a search skip region setting method of claim 4, from the object search target image.
 6. The method of claim 5, wherein the searching comprises associating a reference point of the object search target image with a reference point of the template, setting, when a similarity between a predetermined region including the reference point of the object search target image and the template is lower than a threshold, the search skip region based on the reference point of the object search target image, and searching an exclusion region obtained by excluding the search skip region from the object search target image for the object.
 7. A search skip region setting function generation apparatus comprising: a first detector configured to associate a template used to search a model image for an object with a designated search point on the model image, and to detect a designated search point similarity between the designated search point and the template; a second detector configured to change the designated search point when the designated search point similarity does not exceed an object detection determination threshold, and to detect surrounding search point similarities between a plurality of surrounding search points around the designated search point on the model image and the template when the designated search point similarity exceeds an object detection determination threshold; and a generator configured to estimate a distance between the object and the template based on a high-low frequency of the surrounding search point similarities, to estimate a detection error ratio of the object at a first surrounding search point based on the estimated distance, to compare an allowable detection error ratio that allows a detection error of the object with the estimated detection error ratio, and to generate a function required to set a region where the estimated detection error ratio are less than the allowable detection error ratio as the search skip region of the object.
 8. A search skip region setting apparatus comprising: a setting module configured to set the search skip region based on the function generated by a search skip region setting function generation apparatus of claim
 7. 9. An object search apparatus comprising: a search module configured to search, for the object, an exclusion region obtained by excluding the search skip region, which is set on an object search target image by a search skip region setting apparatus of claim 8, from the object search target image.
 10. The apparatus of claim 9, wherein the search module is configured to associate a reference point of the object search target image with a reference point of the template, to set, when a similarity between a predetermined region including the reference point of the object search target image and the template is lower than a threshold, the search skip region based on the reference point of the object search target image, and to search an exclusion region obtained by excluding the search skip region from the object search target image for the object. 